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Research On Coordinated Dispatch Considering EVs' Charging And Large-scale Wind Power Integration

Posted on:2019-09-26Degree:MasterType:Thesis
Country:ChinaCandidate:L SongFull Text:PDF
GTID:2382330545458985Subject:Electrical engineering
Abstract/Summary:PDF Full Text Request
The global energy consumption in today's world is still dominated by fossil fuels,and the proportion of renewable energy still has a lot of room for improvement.With the increasing depletion of petroleum resources,the renewable resources,especially wind power,has been extensively studied and utilized;On the other hand,in order to respond to the call for energy saving and emission reduction and green travel,electric vehicles(EVs)are developing rapidly in China.EV as a new type of transportation to reduce fossil fuel consumption and reduce emissions,its advantages are gradually revealed.But with the constant growing of the penetration level of wind power generation and EVs,power generation planning and scheduling system will face many new challenges.Taking wind power as an example,the volatility and randomness will influence the system strongly.And there are some studies show that in some parts of China,the level of available wind power is high at night and opposite during day time,namely,the wind power possess an anti-peak-regulation feature.The actual operation experience shows that wind power will cause many uneconomic problems such as increasing reserve capacity and making the thermal units deviate from economic operation point,so the economic and environmental benefits brought by wind power will be counteracted.At the same time,the unordered access to grid by a large amount of EVs could significantly impact the power system in following aspects:(1)Creating a higher peak demand;(2)Increasing power loss;(3)Lowering the voltage and power quality at some certain nodes.For the above-mentioned problems,the coordinated dispatch of wind power and EVs may be a solution to them.The work of this paper is exactly based on the above background and problems.The significant impact on power system brought by a large amount of EVs unordered access to gird can be diminished by guiding them charge orderly,and the ordered charging strategy can also reduce generation adjustment caused by wind power to optimize the generation operation,so the goal of peak-shaving and valley-filling and reduction of operation cost will be achieved.The regional wind power and EVs charging demand prediction model are built up firstly in this paper.Then the value structure of wind power is analyzed to quantitatively describe the influence of wind power fluctuation on the coal consumption characteristics of thermal units.Based on that,the coordinated dispatch of EVs and wind power can be modeled.The power grid is the mainstay of this model,then we set three different charging scenarios according to different charging modes to analyze how the charging modes influence the operation of the grid.The economic efficiency,emission and the amount of discarded wind power are the objectives to optimize the operation.The impact on EV users brought by time-of-use(TOU)price and price elasticity are also considered when the charging scenarios were set to quantitatively analyze how this charging scenario benefit both the grid and EV users.The test case verified the validity of the proposed model and the influence caused by different permeability of EVs was also analyzed.As for the algorithm,Quantum-Inspired Evolutionary Algorithm(QEA)is a novel solution to large-scale nonlinear problems,especially to discrete mathematical problems.This paper adopted QEA as outer level algorithm and made some corresponding improvements to cater to the unit commitment(UC)problem and conventional particle swarm optimization(PSO)was adopted as inner level algorithm to solve economic dispatch(ED)problem.So a bi-level particle swarm algorithm was proposed.
Keywords/Search Tags:electric vehicles(EVs), wind power, unit commitment(UC), economic dispatch(ED), ordered charge, Quantum-Inspired Evolutionary Algorithm(QEA), Particle Swarm Optimization(PSO)
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